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Genetic Algorithms assisted ESATAN Modelling for automatic test verification and other scientific uses of ESATAN modeling at DLR Berlin 19 th European Workshop on Thermal and ECLS Software Noordwijk, 11 th- 12 th October 2005 Riccardo Nadalini ! Summary Thermal Probes overview The inversion Problem Inversion by Modelling Parameter search with Genetic Algorithms Details of the process Other scientific uses of ESATAN Ongoing and Future Developments

Summary - European Space Agency · 2006-03-06 · ˝ˆ˛ˇ˚ ˜ ˘ ˆ ! ˛ ˘ˇˆ˙" Thermal Properties Probes Various probes for heat-related experiments are under study: MUPUS (Rosetta

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Page 1: Summary - European Space Agency · 2006-03-06 · ˝ˆ˛ˇ˚ ˜ ˘ ˆ ! ˛ ˘ˇˆ˙" Thermal Properties Probes Various probes for heat-related experiments are under study: MUPUS (Rosetta

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Genetic Algorithms assisted ESATAN Modelling for automatic test verification

and other scientific uses of ESATAN modeling at DLR Berlin

19th European Workshop on Thermal and ECLS SoftwareNoordwijk, 11th-12th October 2005

Riccardo Nadalini

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Summary

� Thermal Probes overview� The inversion Problem� Inversion by Modelling� Parameter search with Genetic Algorithms� Details of the process� Other scientific uses of ESATAN� Ongoing and Future Developments

Page 2: Summary - European Space Agency · 2006-03-06 · ˝ˆ˛ˇ˚ ˜ ˘ ˆ ! ˛ ˘ˇˆ˙" Thermal Properties Probes Various probes for heat-related experiments are under study: MUPUS (Rosetta

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Thermal Properties ProbesVarious probes for heat-related experiments are under study:

� MUPUS (Rosetta Lander)

� Extase (terrestrial applications)

� HP3 (planetary and asteroidal heat flow and thermal physics)

All share common characteristics� Shape (long thin “rods”)

� Condition (surrounded by investigated material)

� Operation mode (temperature monitored while heating and cooling of the material occurs)

� Aim (measure of T and thermal properties)

Extase and MUPUS (GRM) probes

HP3 thermal measurements concept

Soil

Surface

Mole

Sensor/Heater

Tether

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InversionTo get the desired information (Cp, k) from a measurement an inversion is required. Two possibilities exist:� Analytical (or direct), by use of heat transfer equations

� Low resources

� Well designed for ideal cases, very difficult to deal with losses and disturbances

� Numerical, by simulation of the experiment with a thermal model

� Model verification needed

� Requires modeller and computation time

Example of measurement (Extase)

Page 3: Summary - European Space Agency · 2006-03-06 · ˝ˆ˛ˇ˚ ˜ ˘ ˆ ! ˛ ˘ˇˆ˙" Thermal Properties Probes Various probes for heat-related experiments are under study: MUPUS (Rosetta

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Thermal Modelling (1)

Model characteristics:� Bi-dimensional (axial symmetry)� Parameters included as nodal properties:

� Density (variable)� Cp (constant)� Conductivity (variable)� Contact resistance

(constant)� Depth� Axial distance

Sensors (16)

16 “sensor” layers

Glass Fibre

Foam (insulation)

Kapton layer (sensors)

Probe

Upper boundary layer

Lower boundary layer

10 concentric rings

1

2

15

16

……

Purpose: simulate experiments with a certain combination of physical characteristics. If simulation fits the experimental reality, the characteristics of the material (Cp, l,r, Gsurf) are determined

MUPUS-Extase 2D thermal model

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Thermal Model (2)

Results of first Phases� Model vs. experiment, Manual Fit (3 layers)� Satisfactory fit

10

15

20

25

30

35

0 200 400 600 800 1000 1200 1400 1600

Time (s)

Tem

per

atu

re (°

C)

Experiment, Layer 7

Model, layer 7

Exp.layer 8 (below)

Model, layer 8 (below)

Exp. layer 6 (above)

Model, layer 6 (above)

Page 4: Summary - European Space Agency · 2006-03-06 · ˝ˆ˛ˇ˚ ˜ ˘ ˆ ! ˛ ˘ˇˆ˙" Thermal Properties Probes Various probes for heat-related experiments are under study: MUPUS (Rosetta

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Open Issues

Even if the thermal model approach proves effective, some outstanding issues remain with a manual fit approach:� No certainty that the solution is optimal and unique� Extremely long and tedious procedure (trial and error) to

find the “right” set of parameters� Limited number of parameters (no more than 4-5)

Necessity of automating the process

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Genetic Algorithms

Disadvantages

� Complex to implement

� Computation intensive

Advantages� Automatic� Reach Optimal solution� Allows tens of parameters

Genetic algorithms are the best known method to create original set of parameters, to be then used in thermal models GA mimic biology:

� Parameter = gene� Set of parameters = chromosome

� Fit to environment = error fit

� Survival of the most fit = selection of the smallest error

Page 5: Summary - European Space Agency · 2006-03-06 · ˝ˆ˛ˇ˚ ˜ ˘ ˆ ! ˛ ˘ˇˆ˙" Thermal Properties Probes Various probes for heat-related experiments are under study: MUPUS (Rosetta

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GA fitness

� The median fitness increases quickly at first but slows down approaching the optimum

� The complete parameter space is explored, no local optimum limitation (as per gradient method)

� CPU time for GA (mutation, crossover, selection) depends on parameter set characteristic.

� The error function must be accurately selected

Genetic Algorithms fitness evolution example: individual model fitness (black), population median

(red)

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GA and thermal modelling

Thermal model

Error Evaluation

(GA) Tournament

selection

(GA) Mutation, Crossover

Esatan (fortran) Matlab

Experiment Properties

Numerical Model

Fitness Evaluation

Thermal Layering Data

SimulationData Set

Properties

Genetic Algorithms

ModuleErrorParameters

Experiment

A recursive process is implemented between the model (with an internal error function between simulation and experiment) and the Genetic Algorithms Module

At the programming level, the error evaluation is implemented INSIDE the ESATAN model, while the GA module is in MATLAB.

The two programs exchange data through ASCII text files

Page 6: Summary - European Space Agency · 2006-03-06 · ˝ˆ˛ˇ˚ ˜ ˘ ˆ ! ˛ ˘ˇˆ˙" Thermal Properties Probes Various probes for heat-related experiments are under study: MUPUS (Rosetta

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Scientific Uses of ESATAN

In addition to evaluation of experiments, additional scientific simulations are run at DLR Berlin

� Environmental definition � Orbiter-mounted instruments (e.g. Bepi-Colombo Laser

Altimeter)� Surface and subsurface probes (HP3, IR radiometry)

� Scientific Simulation� Mercury surface conditions� Mars subsurface conditions� Mars subsurface long term (climate) simulations� Asteroid surface and subsurface simulations

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Mercury Soil Model (1)

1D soil model (purely conductive, no ESARAD)Parameters included as nodal properties:� Physical parameters (r,Cp, conductivity)� Radiative transmission factor� Location (longitude, latitude) and surface inclination� Node geometric dimensionsSolar flux is calculated directly in ESATAN (Fortran)� Orbit and rotation dependent� Solar disk effect

Page 7: Summary - European Space Agency · 2006-03-06 · ˝ˆ˛ˇ˚ ˜ ˘ ˆ ! ˛ ˘ˇˆ˙" Thermal Properties Probes Various probes for heat-related experiments are under study: MUPUS (Rosetta

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Mercury Soil Model (2)

� Stability over 1 cycle (for Mercury 1 solar day = 176 Earth days) is obtained automatically by using SOLCYC� Stability is ~0.001K

� Conditions over entire planet are obtained with a REPEAT … UNTIL cycle calling the solution routine with different parameters

� Results are stored in custom files� Custom ASCII files are also used to store conditions in

case of an interruption� 1 global map of Mercury Temperature (0 to 6m depth,

5°x5° grid, over 1 solar day) is obtained in 30-36 hours of calculation.

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Temperature Curves

0,0

0,5

1,0

1,5

2,0-150 -100 -50 0 50 100

Temperature (°C)

Dep

th (

m)

Midnight 8,8 Days before Sunrise 7,0 Days before Sunrise 3,5

Sunrise 1,8 3,5 5,3

7,0 Days after Sunrise 10,6 14,1 Days after Sunrise 17,6

Midday 61,6 Days after Sunrise 79,2 Days after Sunrise Sunset

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Page 8: Summary - European Space Agency · 2006-03-06 · ˝ˆ˛ˇ˚ ˜ ˘ ˆ ! ˛ ˘ˇˆ˙" Thermal Properties Probes Various probes for heat-related experiments are under study: MUPUS (Rosetta

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Temperature Maps

Global Result� 1 hemisphere� Temperature at

3m depth� Relevant contours

� Cold (~233K)

� Hot (~313K)

� “Asymmetric” conditions due to Mercury orbit/rotation

� Some mild regions to be found

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Mars Models

To deal with the Mars case, an expansion of the Mercury Soil Model is required, and many modifications must be added:

� Humidity and Volatile contents parameters� Two phase behaviour (condensation and sublimation of

H2O and CO2 ices)� Interaction with atmosphere� Boundary condition come from Mars climate database,

not from internal calculations� Higher Spatial resolution

Page 9: Summary - European Space Agency · 2006-03-06 · ˝ˆ˛ˇ˚ ˜ ˘ ˆ ! ˛ ˘ˇˆ˙" Thermal Properties Probes Various probes for heat-related experiments are under study: MUPUS (Rosetta

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Ongoing & future developments (1)

IN the Genetic Algorithms assisted modelling field, the following activities are or will be addressed:

� Implementation of different models

� Recoding of Genetic Algorithms from Matlab to Fortran

� Alternative languages for thermal models (FEM based, Femlab, MATLAB)

� Additional thermal models

� Investigation on the possible use of the Genetic Algorithms approach also for automatic verification of engineering tests (e.g. STM tests, material tests)

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In the scientific applications field the following actions are

or will be implemented

� Implementation of Mars models

� Extended sensitivity analyses on Mercury

surface/underground relations

� Investigation on parallelization

� Solution of licensing issues

Ongoing & future developments (2)

Page 10: Summary - European Space Agency · 2006-03-06 · ˝ˆ˛ˇ˚ ˜ ˘ ˆ ! ˛ ˘ˇˆ˙" Thermal Properties Probes Various probes for heat-related experiments are under study: MUPUS (Rosetta

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Acknowledgments

The work has been possible thanks to the help of:Dr. Martin Knapmeyer (DLR Berlin) for the Genetic

AlgorithmsMs. Kathrin Schröer (University of Münster) for the

experimental parts